






'''simple docstring'''





from __future__ import annotations

from typing import TypedDict


class  snake_case__      (					snake_case__):
					a_        =					42
					a_        =					42






def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:






			if not isinstance(UpperCamelCase_			,       UpperCamelCase_		):
						raise TypeError('''The parameter s type must be str.'''		)

			return [s[i:] + s[:i] for i in range(len(UpperCamelCase_		)		)]
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[int]:






			if not isinstance(UpperCamelCase_			,       UpperCamelCase_		):
						raise TypeError('''The parameter s type must be str.'''		)
			if not s:
						raise ValueError('''The parameter s must not be empty.'''		)

			UpperCAmelCase_      :       Optional[Any]          =       all_rotations(UpperCamelCase_		)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			UpperCAmelCase_      :       Any          =       {
			    '''bwt_string''': ''''''.join([word[-1] for word in rotations]		),
			    '''idx_original_string''': rotations.index(UpperCamelCase_		),
			}
			return response
def __UpperCAmelCase   (					A					:			str			,       A					:			Union[str, Any]		)    ->   int:






			if not isinstance(UpperCamelCase_			,       UpperCamelCase_		):
						raise TypeError('''The parameter bwt_string type must be str.'''		)
			if not bwt_string:
						raise ValueError('''The parameter bwt_string must not be empty.'''		)
			try:
						UpperCAmelCase_      :       Any          =       int(UpperCamelCase_		)
			except ValueError:
						raise TypeError(
						    '''The parameter idx_original_string type must be int or passive'''
						    ''' of cast to int.'''		)
			if idx_original_string < 0:
						raise ValueError('''The parameter idx_original_string must not be lower than 0.'''		)
			if idx_original_string >= len(UpperCamelCase_		):
						raise ValueError(
						    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''		)

			UpperCAmelCase_      :       Dict          =       [''''''] * len(UpperCamelCase_		)
			for _ in range(len(UpperCamelCase_		)		):
						for i in range(len(UpperCamelCase_		)		):
									UpperCAmelCase_      :       Optional[int]          =       bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
						_UpperCamelCase						:						List[Any]  =					"Provide a string that I will generate its BWT transform: "
						_UpperCamelCase						:						str  =					input(entry_msg).strip()
						_UpperCamelCase						:						Any  =					bwt_transform(s)
						print(
						    f'''Burrows Wheeler transform for string \'{s}\' results '''
						    f'''in \'{result["bwt_string"]}\''''
						)
						_UpperCamelCase						:						Union[str, Any]  =					reverse_bwt(result['bwt_string'], result['idx_original_string'])
						print(
						    f'''Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '''
						    f'''we get original string \'{original_string}\''''
						)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




